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ANN-modell för att bestämma renoveringsbehov av vattenledningar -- Utvärdering av viktiga attribut med tillämpning för Umeå kommun

Nilsson, Didrik LU (2020) In TVVR20/5010 VVRM05 20201
Division of Water Resources Engineering
Abstract (Swedish)
Sveriges vattenledningsnät kräver kontinuerliga och stora in-
vesteringar och måste underhållas på ett effektivt sätt; syftet med
den här studien var därför att utröna vilka ledningsattribut som
är viktigast för att i en ANN-modell identifiera ledningar med
hög risk för läckage. Detta gjordes genom att först använda attri-
buturvalsmetoderna ReliefF och Recursive Feature Elimination
(RFE) tillsammans med Random Forest Classification (RFC) och
Multinomial logistisk regression (MLR) för att skapa urval av attri-
buten. Dessa grupper av attribut användes sedan i ANN-modellen,
och modellens prestation med respektive urvalsgrupp jämfördes.
ReliefF och RFE med MLR lyckades rangordna attributen, medan
RFE med RFC endast kunde särskilja... (More)
Sveriges vattenledningsnät kräver kontinuerliga och stora in-
vesteringar och måste underhållas på ett effektivt sätt; syftet med
den här studien var därför att utröna vilka ledningsattribut som
är viktigast för att i en ANN-modell identifiera ledningar med
hög risk för läckage. Detta gjordes genom att först använda attri-
buturvalsmetoderna ReliefF och Recursive Feature Elimination
(RFE) tillsammans med Random Forest Classification (RFC) och
Multinomial logistisk regression (MLR) för att skapa urval av attri-
buten. Dessa grupper av attribut användes sedan i ANN-modellen,
och modellens prestation med respektive urvalsgrupp jämfördes.
ReliefF och RFE med MLR lyckades rangordna attributen, medan
RFE med RFC endast kunde särskilja tre attribut som mindre vik-
tiga – dessa rangordningar innebar dock inte att attributen faktiskt
var viktiga – de behövde testas i ANN-modellen först. Studien
visade att antalet attribut kunde begränsas markant: när 10 utvalda
attribut användes uppnåddes en noggrannhet på 0,79, att jämföra
med alla tillgängliga attribut (19 stycken) då en noggranhet på
0,80 erhölls. Effekten av att endast inkludera attribut som är lätta
att anskaffa och som är jämförbara mellan orter undersöktes också
och en modellnoggrannhet på 0,75 uppnåddes då. (Less)
Abstract
Sweden’s water pipe network demands continuous and large
investments and must be maintained in an effective way; the aim
with this study was therefore to investigate which pipe features are
most important to identify pipes with a high risk of leakage in an
ANN-model. This was done by first utilizing the feature selection
methods ReliefF and Recursive Feature Elimination (RFE) with
Random Forest Classification (RFC) and Multinomial Logistic
Regression (MLR) to identify subsets of features seemingly
important to evaluate the risk of leakage. The ANN-model were
then run with these subsets and the difference in accuracy between
subsets were compared. ReliefF and RFE with MLR succeeded
in ranking features, while RFE with RFC only... (More)
Sweden’s water pipe network demands continuous and large
investments and must be maintained in an effective way; the aim
with this study was therefore to investigate which pipe features are
most important to identify pipes with a high risk of leakage in an
ANN-model. This was done by first utilizing the feature selection
methods ReliefF and Recursive Feature Elimination (RFE) with
Random Forest Classification (RFC) and Multinomial Logistic
Regression (MLR) to identify subsets of features seemingly
important to evaluate the risk of leakage. The ANN-model were
then run with these subsets and the difference in accuracy between
subsets were compared. ReliefF and RFE with MLR succeeded
in ranking features, while RFE with RFC only separated three
features as less important—these rankings, though, had to be
tested with the ANN-model to see if the features actually were
important. The study found that the amount of features could be
reduced distinctly: with 10 important features, an accuracy of 0.79
were achieved, to compare with 0.80 when all the 19 available
features were utilized in the model. The effect of only including
features that are easy to obtain and are similar between cities was
also studied, and a model accuracy of 0.75 were obtained with
these attributes. (Less)
Please use this url to cite or link to this publication:
author
Nilsson, Didrik LU
supervisor
organization
course
VVRM05 20201
year
type
H2 - Master's Degree (Two Years)
subject
keywords
underhåll, förnyelse, strategiskt, attributurval, maskininlärning
publication/series
TVVR20/5010
report number
20/5010
ISSN
1101-9824
language
Swedish
additional info
Examiner: Magnus Larson
id
9021828
date added to LUP
2020-06-24 10:29:49
date last changed
2020-06-24 10:29:49
@misc{9021828,
  abstract     = {{Sweden’s water pipe network demands continuous and large
investments and must be maintained in an effective way; the aim
with this study was therefore to investigate which pipe features are
most important to identify pipes with a high risk of leakage in an
ANN-model. This was done by first utilizing the feature selection
methods ReliefF and Recursive Feature Elimination (RFE) with
Random Forest Classification (RFC) and Multinomial Logistic
Regression (MLR) to identify subsets of features seemingly
important to evaluate the risk of leakage. The ANN-model were
then run with these subsets and the difference in accuracy between
subsets were compared. ReliefF and RFE with MLR succeeded
in ranking features, while RFE with RFC only separated three
features as less important—these rankings, though, had to be
tested with the ANN-model to see if the features actually were
important. The study found that the amount of features could be
reduced distinctly: with 10 important features, an accuracy of 0.79
were achieved, to compare with 0.80 when all the 19 available
features were utilized in the model. The effect of only including
features that are easy to obtain and are similar between cities was
also studied, and a model accuracy of 0.75 were obtained with
these attributes.}},
  author       = {{Nilsson, Didrik}},
  issn         = {{1101-9824}},
  language     = {{swe}},
  note         = {{Student Paper}},
  series       = {{TVVR20/5010}},
  title        = {{ANN-modell för att bestämma renoveringsbehov av vattenledningar -- Utvärdering av viktiga attribut med tillämpning för Umeå kommun}},
  year         = {{2020}},
}